提供基于torch的EEMD+ConvLSTM的读取csv数据,前80%为训练集,后20%为测试集的多输入单输出的时序训练及预测代码
时间: 2023-03-06 19:16:20 浏览: 63
以下是基于torch的EEMD ConvLSTM读取csv数据,划分训练集和测试集,多输入单输出的时序训练及预测的代码。请注意,此代码仅提供参考,并需要根据您的具体需求进行调整和修改。
首先,我们需要导入所需的库和模块:
```python
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
```
接下来,我们定义一个自定义数据集类,用于加载和处理CSV数据:
```python
class CustomDataset(Dataset):
def __init__(self, csv_file):
self.data = pd.read_csv(csv_file)
self.X = self.data.iloc[:, :-1].values
self.Y = self.data.iloc[:, -1].values
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.X[index], self.Y[index]
```
然后,我们定义一个EEMD ConvLSTM模型类:
```python
class EEMDConvLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, kernel_size):
super(EEMDConvLSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.kernel_size = kernel_size
self.conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size // 2)
self.eemd = EEMD() # EEMD是一个自定义的类,用于执行EEMD分解
self.lstm = nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, batch_first=True)
self.linear = nn.Linear(self.hidden_size, 1)
def forward(self, x):
x = x.unsqueeze(1)
x = self.conv(x)
x = x.squeeze(1)
x = self.eemd(x)
x = torch.tensor(x)
x = x.permute(0, 2, 1)
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
output, _ = self.lstm(x, (h0, c0))
output = output[:, -1, :]
output = self.linear(output)
return output
```
然后,我们定义一个函数,用于将数据分为训练集和测试集,并加载到DataLoader中:
```python
def prepare_data(csv_file, batch_size, train_size=0.8):
dataset = CustomDataset(csv_file)
train_size = int(train_size * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
return train_loader, test_loader
```
接下来,我们定义一个训练函数,用于执行多输入单输出的时序训练